Mining Generalized Association Rules
Summary: Mining generalized association rules with item taxonomies; proposes Cumulate and EstMerge to avoid full ancestor expansion, achieving 2–5× speedups (often >100×). Introduces a taxonomy-aware interest measure that prunes 40–60% of rules, reducing redundancy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ramakrishnan Srikant (IBM)
- 2. Rakesh Agrawal (IBM)
BibTeX Citation
@article{srikant_vldb95,
title = {{Mining Generalized Association Rules}},
author = {Srikant, Ramakrishnan and Agrawal, Rakesh},
journal = {PVLDB},
series = {{VLDB} '95},
pages = {407},
year = {1995}
}
Incoming Citations (Sorted by Pagerank)
Showing 29 of 29 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.0006567919 |
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 240 | An Effective Hash-Based Algorithm for Mining Association Rules | 1995 | SIGMOD | 0.0002366381 |
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